Notice that [1]} yang2019learning achieves a high performance on in-domain AIDA-CoNLL test set, but doesn't perform well on five out-domain test sets (even inferior than [2]} ganea2017deep). We found that [1]} yang2019learning includes an explicit type similarity which is based on a typing systemIt yields 95% accuracy on AIDA-A according to their paper. trained with AIDA-train NER annotation. This explicit type similarity feature is tailored for AIDA-CoNLL data set and doesn't achieve good generalization performance on out-domain test sets. In contrast, our BERT-Entity-Sim model capturing latent type information has potential better generalization performance with an average 2.10 F1 improvement over them.
